Risk or financial modeling is generally known to refer to the use of formal econometric techniques to determine the aggregate risk in a portfolio, which may be a portfolio of physical and financial assets. Risk modeling may use a variety of techniques and parameters, such as market risk, value at risk, historical simulation, and/or extreme value theory to analyze a portfolio and make forecasts of the possible (or likely) gains or losses that may be incurred across one or more risks, where risks are typically grouped into categories, such as credit risks, commodity market fluctuation risks, liquidity risks, interest rate risks, operational risks, etc.
Many large financial intermediary firms use risk modeling to help portfolio managers assess the amount of capital reserves to maintain and to help guide their purchases and sales of various classes of financial assets. Quantitative risk analysis and modeling have become important in the light of corporate scandals in the past few years (most notably, Enron), Basel II, the revised FAS 123R and the Sarbanes-Oxley Act. In the past, risk analysis was done qualitatively, but the advent of powerful computing software (several exist, like Microsoft Excel, Quantrix Modeler and Risk Simulator and others), quantitative risk analysis can be executed using computer software.
Similarly, cash-flow-at-risk (CFAR) signifies the cash that would be received or paid in a portfolio of transactions with x % of certainty in a given time horizon, which may be yearly, quarterly, etc. CFAR looks at the cash-flow exchange upon settlement of the transaction. Therefore, CFAR is a measure that hedgers can use to identify the risk associated with changes in the prices of commodities that are sold or purchased.
Risk performance estimates are labeled to identify the applicable risk threshold for the measurement period under consideration. These risk thresholds may be expressed as a P-value in the form of P-x, where x is an integer from 1 to 99. For example, P-2 refers to the estimates generated at +2 standard deviations, and P-98 refers to the estimates generated at −2 standard deviations. The area beneath the bell curve that covers the range between +2 standard deviations and −2 standard deviations contains approximately 95% of the expected outcomes for the given portfolio during the measurement period, and approximately 2% of the remaining outcomes lie above and another 2% lie below this range. Therefore, approximately 98% of the expected outcomes can be found in the range above the low case (−2 standard deviations) estimate and 2% above the high case (+2 standard deviations) estimate.
A risk profile is a set of values which describe the theoretical distribution of financial outcomes of the value of a portfolio. The P-50 value is the midpoint of the distribution and is obtained by valuing the portfolio at the market prices. The P-2 and P-98 points of the distribution are determined through CFAR analysis by risking prices +/−2 standard deviations. The value of P-98 will estimate how the low price scenario will affect the portfolio value as measured at a price −2 standard deviations below the mean. The value of P-2 will estimate how the high price scenario will affect portfolio value as measured at a price +2 standard deviations above the mean. Additional points on this theoretical distribution could be determined through analysis to plot this distribution. Changes in the level of P-50 will cause P-2 & P-98 to change. In addition, changes in market volatility will compress or stretch the risk profile.
Consider, for example, how a midstream producer, such as a bio-diesel company that buys soybean oil for conversion to diesel oil, could utilize risk performance estimates. Approximately 8 lbs of soybean oil are needed to produce 1 gallon of diesel fuel. In a typical month, the company may purchase 30-35 million pounds of soybean oil in order to produce approximately 4 million gallons of diesel oil. At market prices, the company may expect 20 million dollars in revenue with a low case (P-98) of −20 million dollars negative revenue and a high case (P-2) of +80 million dollars in positive revenue. The company must determine if this revenue range is acceptable, or if the company should undertake risk mitigation by taking hedging actions to change the revenue risk profile. Possible hedging actions may include engaging in financial hedges, shutting down the plant or selling back the soybeans. The company would also benefit from a feedback mechanism, such as updating the risk performance estimates systematically on a monthly or daily basis, so that the company may monitor changes in the revenue risk profile due to market prices and portfolio changes which occur during the month.
Companies with multiple asset classes have risk profiles that are best modeled by accounting for the non-correlation between assets. For example, consider two separate assets: Asset A and Asset B. The CFAR of Asset A can be determined, as can the CFAR of Asset B. However, if a firm were to own Assets A and B together, the CFAR would not simply be the sum of the two separate firms due to correlations between the two assets. That is,CFAR(Asset A)+CFAR(Asset B)≠CFAR(Asset A and Asset B)  (1)
The correlation between the two assets diversifies the risk of owning both assets and reduces the risk to the company. For example, the merging of an oil producer with a natural gas producer would not produce a company with a risk profile that would be the sum of the parts, because the non-correlation between the two asset classes in the new firm would reduce the company's risk. The precise measurement of this reduced exposure can be modeled as follows:
Owning two or more assets and/or varying asset proportions affects the company's risk profile. Consider, for example, the following firm:Company is short Asset A and long (Asset B+Asset C).  (2)For this example there are the dynamics of three different assets which have additional correlation benefits (A&B), (B&C), and (A&C). There is also the added dynamic that the company needs Asset A (which is short) to generate Assets B&C (which are long). The long positions are represented using positive signs and the short positions are represented using negative signs for the asset volumes and generate additional diversification effects. A general model for a company's risk exposures would be:
                                                        ∑                              s                =                1                            n                        ⁢                          Asset              s                                +                                    ∑                              l                =                1                            m                        ⁢                          Asset              l                                      ,                            (        3        )            where s=the set of short assets and includes 1 to n items, and l=the set of long assets and includes 1 to m items. If desired, a user of a risk management system can also include input fuels needed for this process.
Decision-makers at companies would benefit from the ability to make hedging decisions that lead to the development of a hedge program that supports the achievement of budgetary objectives by converting commodity prices and price curves to risked estimates of budgetary performance based on CFAR analysis using Monte Carlo simulations applied to the combined portfolio of physical and financial assets. Decision-makers could also benefit from a system that uses an iterative method to generate a performance risk profile which would track changes in performance estimates resulting from adjustments to the portfolio of assets. If the decision-makers decide that a change to the risk profile is necessary, the decision-makers may want to consider various changes to the portfolio, and then rerun the process to show the effect of the changes to the portfolio. Further, decision-makers would also benefit from a feedback mechanism, such as a method of updating the risk performance estimates systematically on a monthly or daily basis, so that the decision-makers may monitor changes in the revenue risk profile due to market prices and portfolio changes which occur during the observation period.
In view of the above issues that face decision-makers at a company, decision-makers would benefit from a methodology that is capable of the following: 1) conversion of risked price data to budgetary performance estimates; 2) identification and utilization of the correlation/non-correlation between asset classes to identify likely performance through a range of specified confidence thresholds; 3) dynamic analysis that allows the decision-makers to track the impact that changes in price, volatility and the correlation between asset classes have on enterprise performance; 4) disciplined collection and processing of data in a consistent manner; 5) systematic analysis of data through Monte Carlo simulations to develop a range of potential outcomes specific to the mix of physical and financial assets under review; and 6) periodic analysis of a portfolio at consistent and specific intervals.
Therefore, there is a need in the art for a disciplined, systematic, and periodic methodology to quantify the risk-reward of commodity price exposures to provide decision-makers with the ability to make strategic hedging decisions that are based on enterprise performance rather than commodity price(s), while incorporating all available, relevant and up-to-date market data, and document the value of all hedge decisions to demonstrate the costs and benefits as related to budgetary performance.